Digitalized and scalable pharma anti-counterfeiting with the NIRONE Scanner

With estimated sales, approximately €200 billion per year counterfeit pharmaceuticals are the most lucrative sector of the global trade in illegally copied products. Fraudulent drugs harm or kill millions around the world and inflict serious damage to the brands and profitability of major pharmaceutical manufacturers. Today’s complex globalized economy, online transactions, more and more complex pharmaceutical supply chains have made the discovery, tracking, and policing of the counterfeit drugs increasingly difficult.


Counterfeit products are ones: 

  • without active pharmaceutical ingredients (APIs), including the wrong ingredients. 
  • with incorrect quantities of these APIs, usually containing less than the stated amount. 
  • with fake or counterfeit packaging, which may be copied or modified, to escape prosecution for infringing intellectual property. 


In a narrowest, legal sense, a counterfeit drug is one infringing on a registered trademark. 


Innovative technologies are needed to protect the complex processes (manufacturing and distribution) of medicines to ensure patient safety. These include serialization, authentication, track, and trace, among others. These can be divided into following main categories:

  • Radio frequency identification (RFID)
  • Advanced computational methods
  • Online verification
  • Blockchain technologies


These are usually integrated into different types of anti-counterfeiting solutions as no single method is adequate to prevent counterfeiting and the sales and distribution of counterfeit medicines.


Anti-counterfeiting screening technologies


A variety of screening technologies are employed for a rapid and efficient detection substandard of falsified medicines in the field. These consume fewer resources compared to traditional quality control technologies. The three principle screening technology types are: visual, physical, and chemical.


The visual analysis includes packaging integrity, labelling, dosage units’ inspection, and used product security features such as holograms and microprinting. The physical analysis involves the evaluation of the disintegration and/or dissolution performance and employ microscopy, refractometry, and or refractive index measurements. In the chemical analysis the application of spectroscopy, spectrometry, chromatography, and or wet chemistry are typical methods.


Laboratory analysis of drug samples has long been the method of choice to determine the authenticity of suspect counterfeit pharmaceuticals, which requires varying degrees of irreversible sample preparation (removal from a sealed container or blister pack, crushing into powder, dissolution with solvent, etc.) and therefore cannot be used in non-destructive mode. The laboratory methods such as liquid and gas chromatography, X-ray diffraction, and similar technologies require highly trained laboratory staff with expertise, time, and are expensive. 


Screening technologies comparison

Picture 1. Screening technologies comparison.



The information a technique provides, as well as its reliability, cost, required expertise, speed, and portability make it more or less appropriate in any given situation.


Technologies for field detection of falsified and substandard drugs must be portable, relatively simple to use, sturdy, and inexpensive to buy, use, and maintain and they must also provide reliable, useful data.


Near-infrared (NIR) technology


The availability of portable near-infrared (NIR) and Raman spectrometers have led to an increase in the use of these techniques for drug quality analysis. Both NIR and Raman are nondestructive, fast, and do not require sample preparation and enable the measurement through blister packs. On the other hand, some blister packs, capsule materials, and tablet coatings can interfere with Raman scattering and make readings difficult.


NIR is extremely well suited to quantitative analysis of drug content. Computer modelling may produce limited quantitative characterization from all vibrational spectroscopy. NIR spectra of two different compounds differ only subtly, and accurate interpretation of the results may require significant training. Therefore, for example, machine learning may become a very useful tool to help a more accurate interpretation of the results. 

Picture 1

Picture 2. Near infrared spectral fingerprints can be used to distinguish counterfeited pills from genuine ones


Picture 12


Picture 3. Near infrared spectral responses of reference and measured data.


The limitation of CD3, X-ray Diffraction, Raman, and NIR portable devices is that they depend on the use of reference libraries of pharmaceuticals to identify falsified and substandard products. These libraries must be routinely updated when new products both original or generic or new compounds enter the market, which may limit their feasibility. A very practical approach is not to identify the counterfeits, but to authenticate the original products using a reference library.


NIRONE® Scanner as a globally scalable anti-counterfeit tool


As discussed previously, NIR spectroscopy is a proven technology for the identification of unknown materials. Most laboratory techniques are slow and they require expensive instruments and also highly educated laboratory personnel. NIR spectroscopy is a practical solution when a similar analysis is to be conducted in the field. It offers a cost-efficient, accurate and reliable method to analyze numerous samples in a short time and without any sample preparation. These are important features when an analysis should be done out-of-lab. The added value is created by the price point of the device which is only a small fraction of the cost of instruments widely used in central laboratories. 


There are several portable spectrometers already on the market. The main challenges with these devices relate to their size and price. Large analyzers are cumbersome to use or carry in the field and high price limits their use in specific cases. Spectral Engines has overcome these challenges by developing the NIRONE® Scanner that combines powerful NIR spectroscopy and advanced machine learning algorithms


Portable NIRONE devices can be connected to the cloud and advanced algorithms for identifications can be run from the cloud. This makes it easy to update the spectral signature libraries with new materials and cost-efficiently test new algorithms based on the data of hundreds or even thousands of sensors. 


The NIRONE Scanner with its scalability, AI library models with machine learning, and Cloud computing powers offer a cost-effective efficient scalable method to detect original and authentic drugs at all levels of the supply chain from manufacturing to end-customer.


The key benefits of Spectral Engines’ NIRONE solutions are: 

  • Fast and reliable detection of counterfeits, illegal drugs, and explosives 
  • Rapid, non-destructive measurement, without a need for sample preparation 
  • Affordability 
  • Connectivity and portability 
  • Easy-to-upgrade libraries via cloud-based tools
  • No need for laboratory expertise in field testing


Next-generation of anti-counterfeiting solution


Covert and overt security features such as tracking, serialization, are extensively used to protect pharmaceutical product integrity and identify counterfeits. Unfortunately, counterfeiters are also becoming increasingly sophisticated in their manufacturing processes, responding to anti-counterfeit measures, strategies, being capable of duplicating packaging, barcodes, and seals which are almost impossible to determine the authenticity of the product(s).


Artificial Intelligence (AI) combined with tracing and tracking technology, and even to the blockchain, is a novel idea. While blockchain, IoT, tracking, and tracing technologies can provide a bulk of data, AI-powered analytics can offer additional capabilities that are beyond brand building and protection. Combining data in the blockchain with the NIRONE material fingerprint data of the authenticated drugs can make this method even more powerful. Cloud computing and IoT enable the system to help to identify the original drugs by the original material fingerprint(s) of the products collected at the time of manufacturing. This data is transferred to a global library that is updated and distributed to the scalable network of scanners around the world.


This enables the use of the track and trace data to make real-time supply decisions by analyzing the tracing data and combining it with the on-the-field inspection data on suspected counterfeits. The integrated Artificial Intelligence technology enables to both centralize data management on the packaging lines and integrate the information coming from different sources. This smart data helps to understand the patterns of entry points of counterfeit products and to create a centralized heat map on the appearance of counterfeit medicines, globally. This enables coordinated countermeasures to be taken with local authorities.


The NIRONE Scanner with advanced algorithms, cloud computing, and machine learning enables the inspectors of pharmaceutical companies, regulatory agencies, and health authorities around the world to use this powerful technology in airports, ports, hospitals, and pharmacies by field inspection forces to target counterfeiters.


Key Findings and Conclusions

  • The increasing sophistication of criminals will increase the need for more efficient and possibly expensive technologies to detect falsified medicines
  • No single analytical technique provides enough information to confirm authenticity, but combining techniques gives more precision
  • Easily accessible detection technologies (in low- and middle-income countries) will help curtail the distribution and sales of falsified and substandard medicines
  • Easy to use, inexpensive, and robust field technologies are useful for detecting most falsified and substandard drugs. However, the costs associated with developing these technologies are a barrier to having them available in the field
  • NIRONE Scanner enables the anti-counterfeit actions addressing the key findings and conclusions listed above making it a very powerful tool for anti-counterfeiting




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